Save

Estimation of Censored Regression Model: A Simulation Study

In: Frontiers of Economics in China
Authors:
Chunrong Ai
Search for other papers by Chunrong Ai in
Current site
Google Scholar
PubMed
Close
and
Qiong Zhou
Search for other papers by Qiong Zhou in
Current site
Google Scholar
PubMed
Close
Download Citation Get Permissions

Access options

Get access to the full article by using one of the access options below.

Institutional Login

Log in with Open Athens, Shibboleth, or your institutional credentials

Login via Institution

Purchase

Buy instant access (PDF download and unlimited online access):

We investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including Honoré estimator, Hansen’s best two-step GMM estimator, the continuously updating GMM estimator, and the empirical likelihood estimator (ELE). The latter three estimators are based on more conditional moment restrictions than the Honoré estimator, and consequently are more efficient in large samples. Although the latter three estimators are asymptotically equivalent, the last two have better finite sample performance. However, our simulation reveals that the continuously updating GMM estimator performs no better, and in most cases is worse than Honoré estimator in small samples. The reason for this finding is that the latter three estimators are based on more moment restrictions that require discarding observations. In our designs, about seventy percent of observations are discarded. The insufficiently few number of observations leads to an imprecise weighted matrix estimate, which in turn leads to unreliable estimates. This study calls for an alternative estimation method that does not rely on trimming for finite sample panel data censored regression model.

Content Metrics

All Time Past Year Past 30 Days
Abstract Views 84 35 4
Full Text Views 8 0 0
PDF Views & Downloads 6 1 0